INSTITUTO ARGENTINO DE NIVOLOGIA, GLACIOLOGIA Y CIENCIAS AMBIENTALES
Unidad Ejecutora - UE
Spatial distribution, patterns and source contributions of POPs in the atmosphere of Great Mendoza using the WRF/CALMET/CALPUFF modelling system
ALTAMIRANO, J.C.; LANA, N.B.; LANA, N.B.; RUGGERI, M.F.; PULIAFITO, S.E.; RUGGERI, M.F.; PULIAFITO, S.E.; ALTAMIRANO, J.C.
KeAi Communications Co.
Año: 2020 vol. 6 p. 103 - 113
Global monitoring of Persistent Organic Pollutants (POPs) has allowed the knowledge of levels and distribution around the world as well as the understanding of its transport through the atmosphere. However, there are still some gaps in this regard, especially in some locations, as the case of Great Mendoza, a medium-sized urban area located in the center-west of Argentina. In this work, the WRF/CALMET/CALPUFF modeling system was used to estimate airborne levels of four families of POPs (PCBs, PBDEs, DDTs and HCB) in the study area. The model was validated from measured data obtained from eleven sites using passive air samplers with polyurethane foam disks (PUFs), subsequently analyzed by GC-ECNI/MS. Considering both sets of data, measured and simulated airborne concentrations, five statistical performance metrics were calculated for each family of POP [Mean bias error, (MBE), Fractional Bias (FB), Normalized Mean Square Error (NMSE), Factor of two (Fa2) and Pearson correlation coefficient (r)]. Results exhibited a good agreement between modeled and measured data, showing that WRF/CALMET/CALPUFF modeling system predicts POPs airborne concentrations with reasonable accuracy at a local scale. Model output was used to examine the relative source contribution to ground-level concentrations and to assess the spatial variability of the studied POPs in the study area. Source apportionment showed the prevalence of emissions from open burning of municipal solid waste (ranging from 9% to 90%) on the simulated atmospheric concentrations. HCB presented the lowest mean contribution from this activity (37%) but the highest variability (SD = 20%), followed by PCBs (69 ± 9%), and PBDEs (84 ± 4%). The spatial pattern obtained from simulations exhibited that both, lowest and highest levels predicted by the model, occurred in areas where no samples were taken, suggesting that the real gradient in the POPs air concentrations would be much greater than those reflected by measured data. This work highlights the usefulness of the implementation of an atmospheric dispersion model, not only in the study of air quality and exposure levels but also as a tool for the proper design of monitoring networks, taking into account the time and cost that sampling campaigns take, and the conclusions that are intended to be made from the analysis of the obtained data.